Soohwan Yu

Single image super-resolution using locally adaptive multiple linear regression.

This paper presents a regularized superresolution (SR) reconstruction method using locally adaptive multiple linear regression to overcome the limitation of spatial resolution of digital images. In order to make the SR problem better-posed, the proposed method incorporates the locally adaptive multiple linear regression into the regularization process as a local prior. The local regularization prior assumes that the target high-resolution (HR) pixel is generated by a linear combination of similar pixels in differently scaled patches and optimum weight parameters. In addition, we adapt a modified version of the nonlocal means filter as a smoothness prior to utilize the patch redundancy. Experimental results show that the proposed algorithm better restores HR images than existing state-of-the-art methods in the sense of the most objective measures in the literature.

Push-Broom-Type Very High-Resolution Satellite Sensor Data Correction Using Combined Wavelet-Fourier and Multiscale Non-Local Means Filtering

In very high-resolution (VHR) push-broom-type satellite sensor data, both destriping and denoising methods have become chronic problems and attracted major research advances in the remote sensing fields. Since the estimation of the original image from a noisy input is an ill-posed problem, a simple noise removal algorithm cannot preserve the radiometric integrity of satellite data. To solve these problems, we present a novel method to correct VHR data acquired by a push-broom-type sensor by combining wavelet-Fourier and multiscale non-local means (NLM) filters. After the wavelet-Fourier filter separates the stripe noise from the mixed noise in the wavelet low- and selected high-frequency sub-bands, random noise is removed using the multiscale NLM filter in both low- and high-frequency sub-bands without loss of image detail. The performance of the proposed method is compared to various existing methods on a set of push-broom-type sensor data acquired by Korean Multi-Purpose Satellite 3 (KOMPSAT-3) with severe stripe and random noise, and the results of the proposed method show significantly improved enhancement results over existing state-of-the-art methods in terms of both qualitative and quantitative assessments.

Multisensor Super Resolution Using Directionally-Adaptive Regularization for UAV Images

In various unmanned aerial vehicle (UAV) imaging applications, the multisensor super-resolution (SR) technique has become a chronic problem and attracted increasing attention. Multisensor SR algorithms utilize multispectral low-resolution (LR) images to make a higher resolution (HR) image to improve the performance of the UAV imaging system. The primary objective of the paper is to develop a multisensor SR method based on the existing multispectral imaging framework instead of using additional sensors. In order to restore image details without noise amplification or unnatural post-processing artifacts, this paper presents an improved regularized SR algorithm by combining the directionally-adaptive constraints and multiscale non-local means (NLM) filter. As a result, the proposed method can overcome the physical limitation of multispectral sensors by estimating the color HR image from a set of multispectral LR images using intensity-hue-saturation (IHS) image fusion. Experimental results show that the proposed method provides better SR results than existing state-of-the-art SR methods in the sense of objective measures.

Fast digital zooming system using directionally adaptive image interpolation and restoration.

This paper presents a fast digital zooming system for mobile consumer cameras using directionally adaptive image interpolation and restoration methods. The proposed interpolation algorithm performs edge refinement along the initially estimated edge orientation using directionally steerable filters. Either the directionally weighted linear or adaptive cubic-spline interpolation filter is then selectively used according to the refined edge orientation for removing jagged artifacts in the slanted edge region. A novel image restoration algorithm is also presented for removing blurring artifacts caused by the linear or cubic-spline interpolation using the directionally adaptive truncated constrained least squares (TCLS) filter. Both proposed steerable filter-based interpolation and the TCLS-based restoration filters have a finite impulse response (FIR) structure for real time processing in an image signal processing (ISP) chain. Experimental results show that the proposed digital zooming system provides high-quality magnified images with FIR filter-based fast computational structure.

Low-light image enhancement using variational optimization-based Retinex model

This paper presents an optimization-based low-light image enhancement method using spatially adaptive �������� -norm based Retinex model. The proposed method adaptively enforces the regularization parameter using the spatially adaptive weight map, which is generated using the bright channel prior (BCP) and local variance map. Since the proposed weight map assigns the smaller weight value at the bright and edge region, the proposed method can perform weak noise reduction to preserve the edges and textures. In addition, the simplified version of the proposed method is presented using the FFT and quantized weight values for the application to consumer devices. Experimental results show that the proposed method can provide better enhanced result without the ι2 -norm minimization artifacts at the low computational cost.

Artifact-Free Low-Light Video Enhancement Using Temporal Similarity and Guide Map

This paper presents a low-light video restoration algorithm using similar patches among temporally adjacent frames. The proposed artifact-free low-light video restoration algorithm consists of three steps: 1) brightness enhancement using similar patches among temporally adjacent frames and adaptive accumulation; 2) improved color assignment to reduce color distortion; and 3) image fusion for saturation reduction using the guide map. The proposed brightness enhancement step guarantees not to produce any undesired artifacts because of searching the most similar patches among given set of temporally adjacent frames. The color assignment and fusion steps enable a fully automatic color preservation and average brightness control. Experimental results show that the proposed algorithm can better restore high-quality videos without undesired artifacts such as noise amplification, flicker, color distortion, and brightness saturation. As a result, the proposed algorithm can be implemented in a wide range of digital imaging applications such as video surveillance systems and advanced driver assistance systems.

Flicker-free low-light video enhancement using patch-similarity and adaptive accumulation

Recently, the various low-light enhancement methods have been proposed for consumer electronic devices. Although the existing methods provide the better brightness enhancement results, these methods generate the flicker effects during the video enhancement process. Flicker effects are caused by ignoring the correlation information between adjacent frames (i.e. inter-frames). To minimize the flicker effects, the proposed method uses the patch-similarity and adaptive accumulation in the inter-frames domain, and it makes the mean brightness values of enhanced video frames be preserved. The experimental results show that the proposed method can better improve the video frames without flicker effects than existing enhancement methods, and it can be applied to the image signal processing (ISP) chain for consumer electronic devices.

Directionally adaptive cubic-spline interpolation using optimized interpolation kernel and edge orientation for mobile digital zoom system

This paper presents a novel directionally adaptive cubic-spline interpolation method which is applicable to mobile camera digital zoom systems. The problems of conventional (linear and cubic-spline) and advanced interpolation exhibit blurring and jagging artifacts in the digitally zoomed image. To solve this problem, the proposed method performs directionally adaptive interpolation using the optimal interpolation kernel according to the edge orientation. Experimental results show that the proposed method successfully enlarges images with reduced interpolation artifacts compared with both conventional and advanced interpolation methods. Objective evaluation reveals that the proposed method gives higher peak signal to noise ratio (PSNR) and structural similarity (SSIM) figures.

Wavelets and Wavelet Transform

Wavelet transforms are the most powerful and the most widely used tool in the field of image processing. Wavelet transform has received considerable attention in the field of image processing due to its flexibility in representing non-stationary image signals and its ability in adapting to human visual characteristics. Wavelet transform is an efficient tool to represent an image. The wavelet transform allows multiresolution analysis of an image. The aim of the transform is to extract relevant information from an image. A wavelet transform divides a signal into a number of segments, each corresponding to a different frequency band.

Fourier Analysis and Fourier Transform

The origins of Fourier analysis in science can be found in Ptolemy’s decomposing celestial orbits into cycles and epicycles and Pythagoras’ decomposing music into consonances. Its modern history began with the eighteenth century work of Bernoulli, Euler, and Gauss on what later came to be known as Fourier series. J. Fourier in 1822 [Theorie analytique de la Chaleur] was the first to claim that arbitrary periodic functions could be expanded in a trigonometric (later called a Fourier) series, a claim that was eventually shown to be incorrect, although not too far from the truth. It is an amusing historical sidelight that this work won a prize from the French Academy, in spite of serious concerns expressed by the judges (Laplace, Lagrange, and Legendre) regarding Fourier’s lack of rigor. Fourier was apparently a better engineer than mathematician. Dirichlet later made rigorous the basic results for Fourier series and gave precise conditions under which they applied. The rigorous theoretical development of general Fourier transforms did not follow until about one hundred years later with the development of the Lebesgue integral.