Hojin Cho

Bilateral texture filtering

This paper presents a novel structure-preserving image decomposition operator called bilateral texture filter. As a simple modification of the original bilateral filter [Tomasi and Manduchi 1998], it performs local patch-based analysis of texture features and incorporates its results into the range filter kernel. The central idea to ensure proper texture/structure separation is based on patch shift that captures the texture information from the most representative texture patch clear of prominent structure edges. Our method outperforms the original bilateral filter in removing texture while preserving main image structures, at the cost of some added computation. It inherits well-known advantages of the bilateral filter, such as simplicity, local nature, ease of implementation, scalability, and adaptability to other application scenarios.

Text image deblurring using text-specific properties

State-of-the-art blind image deconvolution approaches have difficulties when dealing with text images, since they rely on natural image statistics which do not respect the special properties of text images. On the other hand, previous document image restoring systems and the recently proposed black-and-white document image deblurring method [1] are limited, and cannot handle large motion blurs and complex background. We propose a novel text image deblurring method which takes into account the specific properties of text images. Our method extends the commonly used optimization framework for image deblurring to allow domain-specific properties to be incorporated in the optimization process. Experimental results show that our method can generate higher quality deblurring results on text images than previous approaches.

Registration Based Non-uniform Motion Deblurring

This paper proposes an algorithm which uses image registration to estimate a non‐uniform motion blur point spread function (PSF) caused by camera shake. Our study is based on a motion blur model which models blur effects of camera shakes using a set of planar perspective projections (i.e., homographies). This representation can fully describe motions of camera shakes in 3D which cause non‐uniform motion blurs. We transform the non‐uniform PSF estimation problem into a set of image registration problems which estimate homographies of the motion blur model one‐by‐one through the Lucas‐Kanade algorithm. We demonstrate the performance of our algorithm using both synthetic and real world examples. We also discuss the effectiveness and limitations of our algorithm for non‐uniform deblurring.

Radial Bright Channel Prior for Single Image Vignetting Correction

This paper presents a novel prior, radial bright channel (RBC) prior, for single image vignetting correction. The RBC prior is derived from a statistical property of vignetting-free images: for the pixels sharing the same radius in polar coordinates of an image, at least one pixel has a high intensity value at some color channel. Exploiting the prior, we can effectively estimate and correct the vignetting effect of a given image. We represent the vignetting effect as an 1D function of the distance from the optical center, and estimate the function using the RBC prior. As it works completely in 1D, our method provides high efficiency in terms of computation and storage costs. Experimental results demonstrate that our method runs an order of magnitude faster than previous work, while producing higher quality results of vignetting correction.

Single-shot High Dynamic Range Imaging Using Coded Electronic Shutter

Typical high dynamic range (HDR) imaging approaches based on multiple images have difficulties in handling moving objects and camera shakes, suffering from the ghosting effect and the loss of sharpness in the output HDR image. While there exist a variety of solutions for resolving such limitations, most of the existing algorithms are susceptible to complex motions, saturation, and occlusions. In this paper, we propose an HDR imaging approach using the coded electronic shutter which can capture a scene with row‐wise varying exposures in a single image. Our approach enables a direct extension of the dynamic range of the captured image without using multiple images, by photometrically calibrating rows with different exposures. Due to the concurrent capture of multiple exposures, misalignments of moving objects are naturally avoided with significant reduction in the ghosting effect. To handle the issues with under‐/over‐exposure, noise, and blurs, we present a coherent HDR imaging process where the problems are resolved one by one at each step. Experimental results with real photographs, captured using a coded electronic shutter, demonstrate that our method produces a high quality HDR images without the ghosting and blur artifacts.

Non-uniform motion deblurring for camera shakes using image registration

This paper presents a novel blind motion deblurring method for dealing with non-uniform blurs caused by camera shakes. While there are recent works for non-uniform motion deblurring [Whyte et al. 2010; Joshi et al. 2010], those approaches either limit the freedom of camera motions or require special hardware. Our method is based on a novel representation of motion blurs, which models the blur effects using a set of homographies [Tai et al. to appear]. This representation can fully describe the motions of camera shakes in 3D world, which cause non-uniform motion blurs. Our main contribution is the blind motion deblurring algorithm associated with this representation. We solve the ill-posed non-uniform point spread function (PSF) estimation problem by transforming it into a well-posed image registration problem that estimates homographies. To improve the robustness of our method, we use two input images for deblurring. Our method is experimented with both synthetic and real world examples, and produces superior deblurring results compared to previous methods.

Lucas-Kanade image registration using camera parameters

The Lucas-Kanade algorithm and its variants have been successfully used for numerous works in computer vision, which include image registration as a component in the process. In this paper, we propose a Lucas-Kanade based image registration method using camera parameters. We decompose a homography into camera intrinsic and extrinsic parameters, and assume that the intrinsic parameters are given, e.g., from the EXIF information of a photograph. We then estimate only the extrinsic parameters for image registration, considering two types of camera motions, 3D rotations and full 3D motions with translations and rotations. As the known information about the camera is fully utilized, the proposed method can perform image registration more reliably. In addition, as the number of extrinsic parameters is smaller than the number of homography elements, our method runs faster than the Lucas-Kanade based registration method that estimates a homography itself.

High dynamic range imaging using coded electronic shutter

High dynamic range (HDR) imaging aims to increase the dynamic range of imaging devices, capturing better representations of target scenes. Since the seminal work of Debevec and Malik [1997], tremendous progress has been achieved utilizing multiple images of different exposures that provide complementary brightness information of a scene. However, their application is limited to static scenes with no motions during the sequential capture of images, because changes between images can cause undesirable artifacts such as ghosts. Special imaging devices such as exposure-filtering masks [Nayar and Mitsunaga 2000] could reduce motion artifacts, but manufacturing costs have limited their practicality.

Analysis of the practical coverage of uniform motions to approximate real camera shakes

Motion blur is usually modeled as the convolution of a latent image with a motion blur kernel, and most of current deblurring methods limit types of motion blurs to be uniform with the convolution model. However, real motion blurs are often non-uniform, and in consequence the methods may not well remove real motion blurs caused by camera shakes. To utilize the existing methods in practice, it is necessary to understand how much the uniform motions (i.e., translations) can approximate real camera shakes. In this paper, we analyze the displacement of real camera motions on image pixels and present the practical coverage of uniform motions (i.e., translations) to approximate complicated real camera shakes. We first analyze mathematically the difference of the motion displacement between the optical axis and image boundary under real camera shakes, then derive the practical coverage of uniform motion deblurring methods when used for real blurred images. The coverage can effectively guide how much one can utilize the existing uniform motion deblurring methods, and informs the need to model real camera shakes accurately rather than assuming uniform motions.