Saumik Bhattacharya

Localized image enhancement

Image enhancement is a well established field in image processing. The main objective of image enhancement is to increase the perceptual information contained in an image for better representation using some intermediate steps, like, contrast enhancement, debluring, denoising etc. Among them, contrast enhancement is especially important as human eyes are more sensitive to luminance than the chrominance components of an image. Most of the contrast enhancement algorithms proposed till now are global methods. The major drawback of this global approach is that in practical scenarios, the contrast of an image does not deteriorate uniformly and the outputs of the enhancement techniques reach saturation at proper contrast points. That leads to information loss. In fact, to the best of our knowledge, no non-reference perceptual measure of image quality has yet been proposed to measure localized enhancement. We propose a fast algorithm to increase the contrast of an image locally using singular value decomposition (SVD) approach and attempt to define some parameters which can give clues related to the progress of the enhancement process.

Background estimation and motion saliency detection using total variation-based video decomposition

As human vision system is highly sensitive to motion present in a scene, motion saliency forms an important feature in a video sequence. Motion information is used for video compression, object segmentation, object tracking and in many other applications. Though its applications are extensive, accurate detection of motion in a given video is complex and computationally expensive for the solutions reported in the literature. Decomposing a video into visually similar and residual videos is a robust way to detect motion salient regions. The existing decomposition techniques require large execution time as the standard form of the problem is NP-hard. We propose a novel algorithm which detects the motion salient regions by decomposing the input video into background and residual videos in much lesser time without sacrificing the accuracy of the decomposition. In addition, the proposed algorithm is completely parallelizable that ensures further reduction in computational time with the use of advanced multicore processors.

High accuracy depth filtering for Kinect using edge guided inpainting

Kinect is an easy and convenient means to calculate the depth of a scene in real time. It is used widely in several applications for its ease of installation and handling. Many of these applications need a high accuracy depth map of the scene for rendering. Unfortunately, the depth map provided by Kinect suffers from various degradations due to occlusion, shadowing, scattering etc. The major two degradations are the edge distortion and shadowing. Edge distortion appears due to the intrinsic properties of Kinect and makes any depth based operation perceptually degraded. The problem of edge distortion removal has not received as much attention as the hole filling problem, though it is considerably important at the post processing stage of a RGB scene. We propose a novel method to remove line distortion in order to construct high accuracy depth map of the scene by exploiting the edge information already present in the RGB image.

Visual Saliency Detection Using Spatiotemporal Decomposition

We propose a novel technique for detection of visual saliency in dynamic video based on video decomposition. The decomposition obtains the sparse features in a particular orientation by exploiting the spatiotemporal discontinuities present in a video cube. A weighted sum of the sparse features along three orthogonal directions determines the salient regions in the video cubes. The weights computed using the frame correlation along three directions are based on the characteristic of human visual system that identifies the sparsest feature as the most salient feature in a video. Unlike the existing methods, which detect the salient region as blob, the proposed approach detects the exact boundaries of salient region with minimum false detection. The experimental results confirm that the detected salient regions of a video closely resemble the salient regions detected by actual tracking of human eyes. The algorithm is tested on different types of video contents and compared with the several state-of-the-art methods to establish the effectiveness of the proposed method.

Total variation based fast video decomposition for artifact restoration

Video decomposition into visually similar part and feature part has gained considerable importance due to its applications in different fields of video processing. Some of the diverse problems that can be handled using this decomposition technique are- background estimation, motion saliency detection, single object tracking, multiple object tracking, artifact detection, compression and various others. Though for the technical integrity, video decomposition becomes an obvious tool for video processing, the present approaches provide solutions which are computationally expensive and non-parallelizable. We propose a novel total variation based decomposition method which is an order of magnitude faster than the existing methods and completely parallelizable. We also propose a novel method for reconstructing old color films affected with partial color artifact (PCA) and blotches using the decomposition technique.

Dehazing of color image using stochastic enhancement

Images captured in presence of fog, haze or snow usually suffer from poor contrast and visibility. In this paper we propose a novel dehazing method to increase visibility from a single view without using any prior knowledge about the outdoor scene. The proposed method estimates a visibility map of the scene from the input image and uses stochastic iterative algorithm to remove fog and haze. The method can be applied to color and grayscale images. Experimental results show that the proposed algorithm outperforms most of the state-of-the-art algorithms in terms of contrast, colorfulness and visibility.

Spatiotemporal Colorization of Video Using 3D Steerable Pyramids

We propose a new technique for video colorization based on spatiotemporal color propagation in the 3D video volume, utilizing the dominant orientation response obtained from the steerable pyramid decomposition of the video. The volumetric color diffusion from the sources that are marked by scribbles occurs across spatiotemporally smooth regions, and the prevention of leakage is facilitated by the spatiotemporal discontinuities in the output of steerable filters, representing the object boundaries and motion boundaries. Unlike most existing methods, our approach dispenses with the need of motion vectors for interframe color transfer and provides a general framework for image and video colorization. The experimental results establish the effectiveness of the proposed approach in colorizing videos having different types of motion and visual content even in the presence of occlusion, in terms of accuracy and computational requirements.

Video Shot Detection & Story Board Generation Using Video Decomposition

With the increasing demand of visual media, video summarization is getting more and more importance in professional content browsing, sorting and selection. It is gaining importance in surveillance to preserve storage space and to reduce user interference in analysing huge video data. Unfortunately, most of the summarization algorithms pick the frames with maximum information to summarize the video, which often make the flow of contents difficult to predict. Video shot boundary detection, on the other hand, is essential for more sophisticated applications, like video stabilization, spatio-temporal restoration, colorization etc., which demand temporal continuity within the group of frames. In this paper, we propose a novel method to detect screen shot boundaries which divides a video in groups with spatio-temporal similarities and to generate video story board using the local feature as well as the global features present in the scene, ensuring that the content of the video remains as much intuitive as possible.

Visual saliency detection using video decomposition

Estimation of salient regions in an input video is an active area of research due to its wide applications. In this paper, we propose a novel algorithm to estimate the eye gaze movement in a video using motion, color and structural cues with minimum outliers. The algorithm is generalized to capture salient information for the videos taken under different camera motions. The entire algorithm is parallelizable and ensures faster estimation of salient regions. Using different standard datasets, the estimations of proposed algorithm are compared with state-of-the-art approaches. It is observed that the proposed method produces estimations closer to the ground-truth eye tracker data with minimum outliers.

Restoration of Archival Videos for Preserving Digital Heritage of India

Ever since the invention of motion pictures at the end of the nineteenth century, movies have played an important role in the cultural evolution of human society. Indian society is no exception. The large archives of monochrome as well as color movies, that are authentic evidences of many social, economic, and cultural changes that India has gone through, rightfully claim a place in our national heritage. Unfortunately, because of various factors namely, aging, improper preservation, inadequate imaging technology, many among these movies are severely degraded and show different visual artifacts. Each of the sources of degradation has a unique characteristic. Hence, it is not possible for a single method to restore all the artifacts faithfully. The degraded movies can be restored manually, but it is time consuming and expensive. we propose a unified approach to detect some of the most commonly appearing artifacts in heritage movies and restore them to achieve a superior visual quality.