Damon L. Tull

Maximum-likelihood parameter estimation for image ringing-artifact removal

At low bit rates, image compression codecs based on overlapping transforms introduce spurious oscillations known as ringing artifacts in the vicinity of major edges. Unlike previous works, we present a maximum-likelihood approach to the ringing-artifact removal problem. Our approach employs a parameter estimation method based on the k-means algorithm with the number of clusters determined by a cluster-separation measure. The proposed algorithm and its simplified approximation are applied to JPEG2000 compressed images. Our results show effective and efficient removal of ringing artifacts.

Maximum likelihood parameter estimation for image ringing artifact removal

At low bit rates, image compression codecs based on overlapping transforms introduce spurious oscillation known as ringing artifacts in the vicinity of major edges. The image quality can be enhanced considerably by removing the artifacts. We present a maximum likelihood approach to the ringing artifact removal problem. Our approach employs a parameter estimation method based on the k-means algorithm with the number of clusters determined by a cluster separation measure. The proposed algorithm and its simplified approximation are applied to JPEG2000 compressed images to demonstrate their effectiveness.

Blocking effect removal using robust statistics and line process

The Gibbs distribution with spatially adaptive clique potential functions is used as the image prior distribution in the coding artifact reduction problem. Unlike previous approaches, the image is divided in regions based on the local statistics and a clique potential function is assigned for each region. A non-differentiable potential function that preserves edge in detailed image regions is introduced. Images recovered by the proposed approach contain reduced coding artifact and improved preservation of details.

Low bit rate video sequence coding artifact removal

The picture quality of video frames encoded at very low-bit rates often suffers from both blocking and ringing artifacts. We present two post-processing methods to mitigate the visual quality degradation caused by these artifacts. To reduce the blocking artifact of decoded images, we substitute IDCT for the lapped orthogonal transform embedded inverse discrete cosine transform (le-IDCT). On the other hand, we post-process the decoded video frames using a nonlinear robust filter to reduce the ringing artifact. Extensive simulation results indicated significant improvement in both objective and subjective visual qualities. The computation overhead incurred due to these quality enhancement operations is quite moderate, and can be easily optimized to achieve real-time operation.

Regularized restoration of partial-response distortions in sporadically degraded images

The complex image degradations observed in (video) image sequences are often due to partial-response mechanisms caused by the physical limitations of practical cameras. The restoration of these distortions require more sophisticated image models that consider the underlying image acquisition process. In this work, a broad class of spatially varying distortions is defined. The observed image is modeled as the physically meaningful superposition of K partially degraded images. In areas where the degradation is due to the partial-response of the sensor to multiple object (regions), the distortion is said to be clustered or sporadic. The sporadic distortions under consideration here are spatially varying, object dependent and difficult to estimate from the observed data. A regularized iterative restoration of sporadically degraded images resulting in K restored images is proposed. The algorithm demonstrates a promising (object based) image restoration approach for video editing applications.

Blocking artifact free inverse discrete cosine transform

This paper presents the generalized lapped biorthogonal transform embedded inverse discrete cosine transform (ge-IDCT) as an alternative to the IDCT. The ge-IDCT with nonlinear weighting in the embedded transform domain can reconstruct the signal with alleviated blockishness. Additional complexity, imposed by the replacement, is trivial thanks to an efficient lattice structure. The proposed ge-IDCT is applied in the JPEG still image compression standard to demonstrate its validity.

Identification of causal non-minimum phase blurs using output cumulants

We propose a new blur identification method based on cumulants of an observed noisy blurred image. We show that the new method is capable of identifying blurs that are nonminimum phase which are often encountered in real applications. The blurred image is modeled as a non-minimum phase moving average (MA) model whose parameters are identified using cumulants of the observed image. The new method is superior to model based maximum likelihood and least squares methods that can not identify non-minimum phase blurs since they utilize second-order statistics which are phase blind.

A risk-based frequency adaptive image restoration

A frequency adaptive choice of the regularization parameter based on an unbiased estimate of the risk is investigated. The method is intended to be used in the restoration of images degraded by a shift invariant blur and additive white Gaussian noise. The results are compared with the more widely studied non adaptive case.