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This paper studies the problem of full reference visual quality assessment of denoised images with a special emphasis on images with low contrast and noise-like texture. Denoising of such images together with noise removal often results in image details loss or smoothing. A new test image database, FLT, containing 75 noise-free ‘reference’ images and 300 filtered (‘distorted’) images is developed. Each reference image, corrupted by an additive white Gaussian noise, is denoised by the BM3D filter with four different values of threshold parameter (four levels of noise suppression).

Visual surface inspection is a challenging task due to the highly inconsistent appearance of the target surfaces and the abnormal regions. Most of the state-of-the-art methods are highly dependent on the labelled training samples, which are difficult to collect in practical industrial applications. To address this problem, we propose a generative adversarial network based framework for unsupervised surface inspection. The generative adversarial network is trained to generate the fake images analogous to the normal surface images.

Counting people automatically in a crowded scenario is important to assess safety and to determine behaviour in surveillance operations. In this paper we propose a new algorithm using the statistics of the spatio-temporal wavelet subbands. A t+2D lifting based wavelet transform is exploited to generate a motion saliency map which is then used to extract novel parametric statistical texture features. We compare our approach to existing crowd counting approaches and show improvement on standard benchmark sequences, demonstrating the robustness of the extracted features.

Uneven illumination and shadows in document images cause a challenge for digitization applications and automated workflows. In this work, we propose a new method to recover un-shadowed document images from images with shadows/uneven illumination. We pose this problem as one of estimating the shading and reflectance components of the given original image. Our method first estimates the shading and uses it to compute the reflectance. The output reflectance map is then used to improve the shading and the process is repeated in an iterative manner.

A large-scale video quality dataset called the VideoSet has been constructed recently to measure human subjective experience of H.264 coded video in terms of the just-noticeable-difference (JND). It measures the first three JND points of 5-second video of resolution 1080p, 720p, 540p and 360p. Based on the VideoSet, we propose a method to predict the satisfied-user-ratio (SUR) curves using a machine learning framework. First, we partition a video clip into local spatial-temporal segments and evaluate the quality of each seg- ment using the VMAF quality index.