Bahadir K. Gunturk

Demosaicking: color filter array interpolation

The author begins by discussing the image formation process. The demosaicking methods are examined in three groups: the first group consists of heuristic approaches. The second group formulates demosaicking as a restoration problem. The third group is a generalization that uses the spectral filtering model given in Wandell.

Multiresolution Bilateral Filtering for Image Denoising

The bilateral filter is a nonlinear filter that does spatial averaging without smoothing edges; it has shown to be an effective image denoising technique. An important issue with the application of the bilateral filter is the selection of the filter parameters, which affect the results significantly. There are two main contributions of this paper. The first contribution is an empirical study of the optimal bilateral filter parameter selection in image denoising applications. The second contribution is an extension of the bilateral filter: multiresolution bilateral filter, where bilateral filtering is applied to the approximation (low-frequency) subbands of a signal decomposed using a wavelet filter bank. The multiresolution bilateral filter is combined with wavelet thresholding to form a new image denoising framework, which turns out to be very effective in eliminating noise in real noisy images. Experimental results with both simulated and real data are provided.

Eigenface-domain super-resolution for face recognition

Face images that are captured by surveillance cameras usually have a very low resolution, which significantly limits the performance of face recognition systems. In the past, super-resolution techniques have been proposed to increase the resolution by combining information from multiple images. These techniques use super-resolution as a preprocessing step to obtain a high-resolution image that is later passed to a face recognition system. Considering that most state-of-the-art face recognition systems use an initial dimensionality reduction method, we propose to transfer the super-resolution reconstruction from pixel domain to a lower dimensional face space. Such an approach has the advantage of a significant decrease in the computational complexity of the super-resolution reconstruction. The reconstruction algorithm no longer tries to obtain a visually improved high-quality image, but instead constructs the information required by the recognition system directly in the low dimensional domain without any unnecessary overhead. In addition, we show that face-space super-resolution is more robust to registration errors and noise than pixel-domain super-resolution because of the addition of model-based constraints.

Validity of the Remote Food Photography Method (RFPM) for estimating energy and nutrient intake in near real-time.

Two studies are reported; a pilot study to demonstrate feasibility followed by a larger validity study. Study 1s objective was to test the effect of two ecological momentary assessment (EMA) approaches that varied in intensity on the validity/accuracy of estimating energy intake (EI) with the Remote Food Photography Method (RFPM) over 6 days in free‐living conditions. When using the RFPM, Smartphones are used to capture images of food selection and plate waste and to send the images to a server for food intake estimation. Consistent with EMA, prompts are sent to the Smartphones reminding participants to capture food images. During Study 1, EI estimated with the RFPM and the gold standard, doubly labeled water (DLW), were compared. Participants were assigned to receive Standard EMA Prompts (n = 24) or Customized Prompts (n = 16) (the latter received more reminders delivered at personalized meal times). The RFPM differed significantly from DLW at estimating EI when Standard (mean ± s.d. = −895 ± 770 kcal/day, P < 0.0001), but not Customized Prompts (−270 ± 748 kcal/day, P = 0.22) were used. Error (EI from the RFPM minus that from DLW) was significantly smaller with Customized vs. Standard Prompts. The objectives of Study 2 included testing the RFPMs ability to accurately estimate EI in free‐living adults (N = 50) over 6 days, and energy and nutrient intake in laboratory‐based meals. The RFPM did not differ significantly from DLW at estimating free‐living EI (−152 ± 694 kcal/day, P = 0.16). During laboratory‐based meals, estimating energy and macronutrient intake with the RFPM did not differ significantly compared to directly weighed intake.

Super-resolution reconstruction of compressed video using transform-domain statistics

Considerable attention has been directed to the problem of producing high-resolution video and still images from multiple low-resolution images. This multiframe reconstruction, also known as super-resolution reconstruction, is beginning to be applied to compressed video. Super-resolution techniques that have been designed for raw (i.e., uncompressed) video may not be effective when applied to compressed video because they do not incorporate the compression process into their models. The compression process introduces quantization error, which is the dominant source of error in some cases. In this paper, we propose a stochastic framework where quantization information as well as other statistical information about additive noise and image prior can be utilized effectively.

Quantification of food intake using food image analysis

Measuring free-living peoples’ food intake represents methodological and technical challenges. The Remote Food Photography Method (RFPM) involves participants capturing pictures of their food selection and plate waste and sending these pictures to the research center via a wireless network, where they are analyzed by Registered Dietitians to estimate food intake. Initial tests indicate that the RFPM is reliable and valid, though the efficiency of the method is limited due to the reliance on human raters to estimate food intake. Herein, we describe the development of a semi-automated computer imaging application to estimate food intake based on pictures captured by participants.

High-resolution image reconstruction from multiple differently exposed images

Super-resolution reconstruction is the process of reconstructing a high-resolution image from multiple low-resolution images. Most super-resolution reconstruction methods assume that exposure time is fixed for all observations, which is not necessarily true. In reality, cameras have limited dynamic range and nonlinear response to the quantity of light received, and exposure time might be adjusted automatically or manually to capture the desired portion of the scenes dynamic range. In this letter, we propose a Bayesian super-resolution algorithm based on an imaging model that includes camera response function, exposure time, sensor noise, and quantization error in addition to spatial blurring and sampling.

Fast Bilateral Filter With Arbitrary Range and Domain Kernels

In this paper, we present a fast implementation of the bilateral filter with arbitrary range and domain kernels. It is based on the fast bilateral filter approximation that uses uniform box domain kernel. Instead of using a single box kernel, multiple box kernels are used and combined optimally to approximate an arbitrary domain kernel. The method achieves better approximation of the bilateral filter compared to the single box kernel version with little increase in computational complexity.

A new image denoising method based on the bilateral filter

In this paper we propose a new method to reduce noise in digital images. The method is based on the bilateral filter. The bilateral filter is a nonlinear filter that does spatial averaging without smoothing edges. The spatial averaging aspect of the bilateral filter is very crucial; the bilateral filter has been shown to work better than wavelet thresholding in some recent papers. The proposed method improves the bilateral filter through decomposing a signal into its frequency components. In this way, noise in different frequency components can be eliminated. Experimental results with both simulated and real images are given. In addition to this new method, we also provide an empirical study of the optimal parameter selection for the bilateral filter.

Superresolution under photometric diversity of images

Superresolution (SR) is a well-known technique to increase the quality of an image using multiple overlapping pictures of a scene. SR requires accurate registration of the images, both geometrically and photometrically. Most of the SR articles in the literature have considered geometric registration only, assuming that images are captured under the same photometric conditions. This is not necessarily true as external illumination conditions and/or camera parameters (such as exposure time, aperture size, and white balancing) may vary for different input images. Therefore, photometric modeling is a necessary task for superresolution. In this paper, we investigate superresolution image reconstruction when there is photometric variation among input images.