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ENHANCING UNDERWATER IMAGES USING PIECEWISE

LINEAR SMOOTHING GRADIENT GUIDED FILTER

A. Chrispin Jiji

Assistant Professor, Department of Electronics and Communication Engineering, The Oxford

College of Engineering, Bangalore-560068 and aliated to Visvesvaraya Technological

University, Belagavi, Karnataka, (India).

E-mail: chrispinjij@gmail.com ORCID: https://orcid.org/0000-0001-5267-788X

N. Ramrao

Vice Chancellor, Kalasalingam University, Srivilliputtur, Tamilnadu, (India).

E-mail: nagaraj.ramrao@gmail.com ORCID: https://orcid.org/0000-0003-2542-5999

Recepción:

05/12/2019

Aceptación:

30/12/2019

Publicación:

23/03/2020

Citación sugerida:

Chrispin Jiji, A., y Ramrao, N. (2020). Enhancing underwater images using piecewise linear smoothing

gradient guided lter. 3C Tecnología. Glosas de innovación aplicadas a la pyme. Edición Especial, Marzo 2020,

129-139. http://doi.org/10.17993/3ctecno.2020.specialissue4.129-139

Suggested citation:

Chrispin Jiji, A., & Ramrao, N. (2020). Enhancing underwater images using piecewise linear smoothing

gradient guided lter. 3C Tecnología. Glosas de innovación aplicadas a la pyme. Edición Especial, Marzo 2020,

129-139. http://doi.org/10.17993/3ctecno.2020.specialissue4.129-139

130 http://doi.org/10.17993/3ctecno.2020.specialissue4.129-139

3C Tecnología. Glosas de innovación aplicadas a la pyme. ISSN: 2254 – 4143 Edición Especial Special Issue Marzo 2020

ABSTRACT

Poor visibility owing towards illumination absorption and scattering is challenging

for processing undersea descriptions. However, for enhancing true scene from such

degradation is more important. Unfortunately, existing methods cause gradient reversal

artifact particularly near boundaries. To get better insight of undersea imagery, we project

a piecewise linear smoothing Gradient Guided Filter (P-GGF) technique is to defeat the

diculties caused by conventional schemes, hence produce sharper boundaries based on

GGF and smoothed output based on piecewise linear model. The projected technique

mainly functional for smoothing, ash and feathering. Tentative results prove that the

resultant algorithm can produce imagery with improved ocular excellence than existing

methods.

KEYWORDS

Image Enhancement, Guided Filter, Piecewise liner smoothing, Piecewise constant

smoothing.

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1. INTRODUCTION

Discovering an unexplained undersea globe has paying attention in modern era. Clear

descriptions in oceanic surroundings take part a signicant task in discovering as well as

inspecting underneath globe, namely to observe oceanic biodiversity, undersea salvage,

perceiving submerged tube drip, undersea computer visualization applications etc. Here

the submerged imaging nds a large-scale application. Some diculty comes all throughout

underneath descriptions of illumination absorption as well as scattering. Deep-rooted design

of ocean also composes complications in undersea. On the design of the ocean, reection

of the illumination alters. The reected radiance is horizontally polarized along with its

halfway gets inside the water vertically. Vertical polarization has signicant property that

hatches the substance not as great shining and aides on the way to capture deep colors. One

more diculty in underneath descriptions associated towards underneath density as 800

bits impenetrable than air. Hence when beam travels from air into water, it is halfway back

and, in the meantime, partially enters the water. As we go deeper into ocean, dimension

of beam underneath starts reducing. The underneath molecules absorb assertive size of

beam and create problem for capturing imagery. That is the reason; undersea descriptions

are getting darker as depth increases. The color with shorter wavelength travels remoteness

as compared to longer wavelength. This is why undersea descriptions conquered only with

blue color as in Torres-Méndez and Dudek (2005), Chiang and Chen (2012).

There are many challenges that enhance visibility of corrupted descriptions. While

weakening of submerged descriptions outcomes the combination of multiplicative as well

as additive procedures in Schettini and Corchs (2010) conventional improvement system

namely contrasts alteration, histogram equalizer is robustly defective for such assignment.

Former mechanism to review in section II, diculty was attempted with customized

attainment strategy by several imagery in Narasimhan and Nayar (2003), specic module in

He and Seet (2004) or divergence methods in Schechner and Averbuch (2007). Regardless

of their accomplishment, above approaches undergo various problems which degrade the

system performance.

In contrast, this paper proposes new method for enhancing undersea descriptions

using Piecewise linear smoothing. Our approach uses Piecewise linear smoothing gradient

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guided lter for getting better picture. Numerous spatial eld schemes use bilateral scheme,

which causes blurring and gradient deformation. Gradient Guided representation performs

ltering process using guidance picture substance. Thus, a boundary preservation method

mainly to improve excellence of underneath descriptions.

This paper prepared as follows. Foremost, in Section 2, we concise the existing schemes.

Section 3 introduces a comparison of conventional and projected lters. Section 4 describes

a new method for enhancing undersea descriptions. In Section 5, we describe tentative

outcomes and at last Section 6 conclude our method.

2. RELATED WORK

Edge-preserving smoothing is the fundamental processing procedure within several low-

level computer visualization applications in Farban, Fattal and Lischinski (2010), Farbman,

Fattal, Lischinski, and Szeliski (2008), Gastal and Oliveira (2011; 2012). Meant for on whole

smooth lters believe the smoothed output imagery are piecewise constant. Generally, the

edge-preserving techniques using conned ltering to keep sharp boundaries. Bilateral lter

is extensively used because of its eortlessness Tomasi and Manduchi (1998). Conversely,

it undergoes unwanted sharpening of edges may show undesired proles around edges.

Guided lter introduced in He, Sun, and Tang (2013) overcome these problems but show

unwanted smoothing edges. Weighted GF in Li et al. (2015) uses gradient-domain constrains

for smoothing the picture elements but in few cases, it cannot preserve the boundaries. The

gradient domain GIF in Kou, Chen, Wen, and Li (2015) incorporates a precise initial-order

boundary-aware restraint to keep up boundaries better in some cases.

These conventional schemes are typically denoted as local model which causes artifact such

as gradient reversals, hence may not ne for few cases. For those schemes, a piecewise linear

form preferred mostly for properly smooth out boundaries. So, no artifacts are present in

improved results. In Liu et al. (2018) piecewise linear method via guided representation

accurately resolve diculty of gradient-reversal except that only some cases illustrate small

smoothing boundaries.

Therefore, we project a P-GGF to properly sharp, smooth all boundaries as well as do

artifacts free enhanced result. Three major goals of projected sections as follows:

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1. To take away gradient reversal, we use Gradient Guided Filter (GGF) to give sharper

boundaries.

2. Next, we use piecewise linear smoothing to smooth the boundaries.

3. The projected module uses P-GGF to produce improved output.

Experimental results produced by P-GGF can perfectly remove the problems caused by the

conventional method.

3. PIECEWISE LINEAR SMOOTHING

Conventional methods mostly suitable for image regions more likely be piecewise linear may

cause artifacts. Clearly, detail layers cannot correctly say details in the original descriptions.

Thus, gradient reversal artifacts exist in their enhanced imagery. In highlighted regions,

two kinds of smoothing can properly keep strong gradients which means strong edges

are properly preserved. For weak edges of small gradients which should be smoothed

out. However, these gradients are not properly smoothed by conventional methods or

even improperly sharpened. As a result, gradient reversal artifacts exist with the enhanced

representation.

For most cases, conventional methods usually need a huge number of bins to avoid

quantization artifacts. Smoothed and enhanced descriptions achieved with rst smoothing

of conventional lters and later uses smooth gradient for reconstructing the image. The

reason for these phenomena is clear. The classical smoothing performed in intensity domain

where intensity values could be very large. In contrast, to overcome all problems we go for

proposed method namely piecewise linear method.



Input Image Transformation Decomposition Scalling

Post

processing

Enhanced

Image

Figure 1. Block diagram of Proposed Method.

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4. PROPOSED METHOD

As depicted in Figure 1 our technique is mainly for enhancing underwater images using

piecewise linear smoothing. Initially, original picture developed by RGB or HSV color

space. RGB depict colors in combination of primary colors.

HSV describes colors namely Hue, Saturation, and Value. Color depiction acting an

essential task, HSV form often chosen over RGB form and express color likewise to how

human vision lean-to recognize color. Later, we used rst layer decomposition by smoothing

the image using L1L0 smoothing and second layer decomposition for sharpening the edges

using gradient guided lter. Next, scaling to vary ocular form of a picture. Finally, post

processing of scaled image to enhance the quality of the image.

The conventional method can simply modeled as:

(1)

where

represents expected output pixel rate at location

, denotes expected

constant rate of pixel values inside kth area in the picture indicated as

. In contrast to

conventional model assumption, projected method can signify:

(2)

where

denotes expected output pixel value at position p, represents pixel value of

guidance image at position p.

and b

stay steady in

. These methods show no

gradient reversal artifacts.

In this paper, we assume a linear form which is spatially linear dierent from gradient

guided image lter. In addition, our method also focuses on how to do projected method

using classical methods.

Dierent from piecewise linear function, we formulate imagery as:

(3)

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where as well as b

denotes some linear coecients which assumed to stay

constant inside

. The above equation indicates linear polynomial regression to remove

stair-casing eect. The model in (3) is general and abstract form which is not used for

explicit formulations of lters. However, when we take the derivative of

with respect

to p, then we have:

(4)

Note that

signies gradient of picture at p. For linear form, their gradients are piecewise

constant. Though, dissimilarity assumes resultant imagery of smoothed output gradients.

Conversely, problem is that we cannot simply reconstruct picture only by its smoothed

gradients. The diculty in using original and its ltered gradients is to reconstruct ltered

image. For original picture, its x-axes and y-axes gradients denoted as

and . By

denoting smoothing process of piecewise constant lters as

, then nal representation

reconstructed by minimizing the following energy function:

(5)

From this, we can execute piecewise linear smoothing through conventional scheme in the

following two steps:

(1). Smoothing x-axes in addition to y-axes, gradients

and of original representation

with . The smoothed output gradients are denoted by and

(2). Using (5) for enhancing picture

from

and

with a proper

value of β. Then the enhanced

is spatially piecewise linear as modelled in (3).

5. EXPERIMENTAL RESULTS

This section presents a comparison of improved outcome produced via ours and conventional

schemes through subjective evaluation of test scene. Figure 2 gives improved result than

conventional methods. As seen from Figure 2(a)-(f), all schemes get better output to some