Many image-processing packages may have capabilities such as those described here.
(NOTE: References to non-USGS products do not constitute endorsement by the U.S. Government.)
Historically, the USGS produced gap-filled products using two methods: Phase One, which used a full Landsat 7 image (pre 2003) to fill the gaps of the SLC off scene, and Phase Two, which incorporated more than two SLC-off scenes together to create a final product. While we no longer offer the gap-filled data products, the methodology used for both methods is available:Phase One: SLC-off to SLC-on
Multiple SLC-off images are required to utilize this method. Individual bands of each image need to be gap-filled before creating a 3-band image. For instance, in order to gap-fill Image1 with Image2, a mosaic will need to be made of Band1 from Image1 and Image2 together. The bands can then be stacked to create the RGB image.
Figure 1. Combining scenes in ERDAS Imagine ™
While it still has a small residual gap, the image below is a combination of the two scenes (without histogram correction applied).
Figure 2. Combined scenes without Histogram Correction in ERDAS Imagine ™
These articles describe how scientists are using Landsat 7 gapped data. This is not a complete listing - many more examples can be found.
E.H. Helmer, Thomas S. Ruzycki, Jay Benner, Shannon M. Voggesser, Barbara P. Scobie, Courtenay Park, David W. Fanning, Seepersad Ramnarine. Detailed maps of tropical forest types are within reach: Forest tree communities for Trinidad and Tobago mapped with multiseason Landsat and multiseason fine-resolution imagery. Forest Ecology and Management
Jin Chen, Xiaolin Zhu, James E. Vogelmann,Feng Gao, Suming Jin A simple and effective method for filling gaps in Landsat ETM+ SLC-off images. Remote Sensing of the Environment
B. Zheng, J.B. Campbell, and K.M. de Beurs. In press (as of Nov. 2011). Remote sensing of crop residue cover sing multi-temporal Landsat imagery. Remote Sensing of Environment.
V. Kovalskyy, D.P. Roy, X.Y. Zhang, and J. Ju. 2012 (online Aug 2011). The suitability of multi-temporal web-enabled Landsat data NDVI for phenological monitoring - a comparison with flux tower and MODIS NDVI. Remote Sensing Letters. Volume 3, Issue 4, Pages 325-334.
P. Potapov, S. Turubanova, and M. Hansen. 2011. Regional-scale boreal forest cover and change mapping using Landsat data composites for European Russia. Remote Sensing of Environment. Volume 115, Issue 2, Pages 548-561.
D.P. Roy, J. Ju, K. Kline, P.L. Scaramuzza, V. Kovalskyy, M. Hansen, T.R. Loveland, E. Vermote, and C. Zhang. 2010. Web-enabled Landsat Data (WELD): Landsat ETM+ composited mosaics of the conterminous United States. Remote Sensing of Environment, Volume 114, Issue 1, Pages 35-49.
M.J. Pringle, M. Schmidt, and J.S. Muir. 2009. Geostatistical interpolation of SLC-off Landsat ETM+ images. ISPRS Journal of Photogrammetry and Remote Sensing. Volume 64, Issue 6, Pages 654-664.
S.K. Maxwell, G.L. Schmidt, and J.C. Storey. 2007. A multi-scale segmentation approach to filling gaps in Landsat ETM+ SLC-off images. International Journal of Remote Sensing. Volume 28, Issue 23, Pages 5339-5356.
C. Zhang, W. Li, and D. Travis. 2007. Gaps-fill of SLC-off Landsat ETM+ satellite image using a geostatistical approach. International Journal of Remote Sensing. Volume 28, Number 22, Pages 5103-5122.