Lanczos Filtering in One and Two Dimensions

By C. Duchon. 1979

Subfields: Numerical Methods & Scientific Computing, Signal Processing

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Numerical Methods & Scientific Computing

1. M. Blackburn
2. I. Orlanski
3. P. Haertel
4. F. Jin
5. J. Molinari
6. Clio Michel
7. Vika Grigorieva
8. N. Lau

In Signal Processing

1. Ferenc Huszár
2. Jie Zhou
3. Bernhard Schölkopf
4. Wei Han
5. Jiahui Yu
6. K. Yu
7. M. Blackburn
8. I. Orlanski

Citations in the corpus

• Photo-Realistic Single Image Super-Resolution Using a Generative Adversarial Network (2016, background): “While these filtering approaches, e.g. linear, bicubic or Lanczos [13] filtering, can be very fast, they oversimplify the SISR problem and usually yield solutions with overly smooth textures.”
• EnhanceNet: Single Image Super-Resolution Through Automated Texture Synthesis (2016, background): “Early interpolation methods such as bicubic and Lanczos [11] are based on sampling theory but often produce blurry results with aliasing artifacts in natural images.”
• Accurate Image Super-Resolution Using Very Deep Convolutional Networks (2015, methodology): “Early methods include interpolation such as bicubic interpolation and Lanczos resampling [7] more powerful methods utilizing statistical image priors [20, 13] or internal patch recurrence [9].”
• End-to-End Image Super-Resolution via Deep and Shallow Convolutional Networks (2016, unknown): “, interpolation-based [27], [28], reconstruction”
• SRFeat: Single Image Super-Resolution with Feature Discrimination (2018, methodology): “Early methods are based on simple interpolation such as bicubic and Lanczos interpolation [17].”
• Anchored Neighborhood Regression for Fast Example-Based Super-Resolution (2013, methodology): “Super-resolution algorithms can be roughly subdivided into three subclasses: interpolation methods like Lanczos upsampling [5] and New Edge Directed Interpolation (NEDI) [10], multi-frame methods [6, 7, 11] which make use of the presence of aliasing in multiple frames of the same scene to produce on”
• Image Super Resolution Based on Fusing Multiple Convolution Neural Networks (2017, methodology): “Early methods use efficient interpolations such as bilinear, bicubic, or Lanczos filtering [21].”
• Recovering Realistic Texture in Image Super-Resolution by Deep Spatial Feature Transform (2018, methodology): “Early methods explore a smoothing prior such as bicubic interpolation and Lanczos resampling [11].”
• The JRA-55 Reanalysis: Representation of Atmospheric Circulation and Climate Variability (2016, methodology): “To extract the various atmospheric waves or disturbances, we apply a Lanczos filter (Duchon 1979) to the daily OLR and the daily zonal and meridional wind components of the reanalysis products.”
• Learned Image Downscaling for Upscaling Using Content Adaptive Resampler (2019, background): “, the Lanczos filter [13], tend to produce ringing artifacts near strong image edges.”
• The MJO as a Dispersive, Convectively Coupled Moisture Wave: Theory and Observations (2016, unknown): “…mean and first three harmonics of the annual cycle, based on the 1979–2011 reference period for OLR and ERA-Interim data, 1996– 2011 for GPCP rainfall, and 1998–2011 for SSM/I–TMI PW. Additionally, a 101-point Lanczos filter (Duchon 1979) is used to extract the anomalies on the 20–100-day time scale.”
• Dynamics of 2-Day Equatorial Waves (2004, methodology): “porally filter (Duchon 1979) the average TB over the IFA, retaining fluctuations between 1.”
• Frame-Recurrent Video Super-Resolution (2018, methodology): “Super-resolution is a classic ill-posed inverse problem with approaches ranging from simple interpolation methods such as Bilinear, Bicubic and Lanczos [9] to example-based super-resolution [12, 13, 40, 42], dictionary learning [32, 43], and self-similarity approaches [16, 41].”
• Characteristics of the Atlantic Storm-Track Eddy Activity and Its Relation with the North Atlantic Oscillation (2005, background): “Spatial and Temporal Filters Most of the figures involve the 31-point Lanczos high-pass filter with a 12-day cutoff that separates the synoptic high-frequency signal from the rest of the flow [see Duchon (1979) for a description of the Lanczos filters].”